Design and Implementation of a Fuzzy-Modified Ant Colony Hardware Structure for Image Retrieval

In this paper, a hardware implementation of a fuzzy modified ant colony processor that is suitable for image retrieval is presented for the first time. The proposed method utilizes three different descriptors in a two stage fuzzy ant algorithm where the query image represents the nest and the database images represent the food. From the hardware point of view, only a small number of algorithms for hardware implementation have been reported in the image retrieval literature, since research focuses mainly on possible software solutions and the acceleration of existing algorithms. The proposed digital hardware structure is based on a sequence of pipeline stages, while parallel processing is also used in order to minimize computational times. It is capable of performing the extraction and comparison of features from (64times64)-pixel-size color images, although through a simple transformation it can be easily expanded to accommodate images of larger sizes. The architecture of the processor is generic; the units that perform the fuzzy inference can be used with different descriptors than the ones proposed here and can be utilized for other fuzzy applications. It was designed, compiled, and simulated using the Quartus Programmable Logic Development System by the Altera Corporation. The fuzzy processor exhibits a level of inference performance of 800 K fuzzy logic inferences per second with 24 rules, and can be used for real-time applications where the need for short processing times is of the utmost importance.